49,621 research outputs found
A Finite Time Analysis of Two Time-Scale Actor Critic Methods
Actor-critic (AC) methods have exhibited great empirical success compared
with other reinforcement learning algorithms, where the actor uses the policy
gradient to improve the learning policy and the critic uses temporal difference
learning to estimate the policy gradient. Under the two time-scale learning
rate schedule, the asymptotic convergence of AC has been well studied in the
literature. However, the non-asymptotic convergence and finite sample
complexity of actor-critic methods are largely open. In this work, we provide a
non-asymptotic analysis for two time-scale actor-critic methods under
non-i.i.d. setting. We prove that the actor-critic method is guaranteed to find
a first-order stationary point (i.e., ) of the non-concave performance function
, with sample
complexity. To the best of our knowledge, this is the first work providing
finite-time analysis and sample complexity bound for two time-scale
actor-critic methods.Comment: 45 page
Hierarchical Knowledge-Gradient for Sequential Sampling
We consider the problem of selecting the best of a finite but very large set of alternatives. Each alternative may be characterized by a multi-dimensional vector and has independent normal rewards. This problem arises in various settings such as (i) ranking and selection, (ii) simulation optimization where the unknown mean of each alternative is estimated with stochastic simulation output, and (iii) approximate dynamic programming where we need to estimate values based on Monte-Carlo simulation. We use a Bayesian probability model for the unknown reward of each alternative and follow a fully sequential sampling policy called the knowledge-gradient policy. This policy myopically optimizes the expected increment in the value of sampling information in each time period. Because the number of alternatives is large, we propose a hierarchical aggregation technique that uses the common features shared by alternatives to learn about many alternatives from even a single measurement, thus greatly reducing the measurement effort required. We demonstrate how this hierarchical knowledge-gradient policy can be applied to efficiently maximize a continuous function and prove that this policy finds a globally optimal alternative in the limit
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